1f4477bad7af3616c1f933a02bfabe4e-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. 'Learning Gaussian Graphical Models with Observed or Latent FVSs' addresses the problem of learning (i.e. The motivation is that exact inference under these models can be done quickly, and so in the case where one needs near-linear inference (which is prohibited in general for sparse GGMs) it is desirable to have this form. The results address three cases: (4.1.1) In (4.1.2) they make the observation that one can exhaustively run the previous algorithm for all k-sets selecting the one that maximizes the likelihood and then provide a greedy algorithm.